Predictive Maintenance is the mechanism performed to prevent faults from occurring, parts adjustments, parts cleaning and parts replacement. Using predictive maintenance, the life of machine, animal or any entity can be predicted. Certain measures need to taken according to the data gathered from various condition monitoring sensors and techniques.
Predictive maintenance can be achieved through :
- Planned Maintenance: Planned maintenance needs a lot of human intervention and monitoring. If the machine fails, it impacts the business economically.
- Recognising Anomalous Behaviours: With the help of certain machine learning techniques and deep learning algorithm anomaly can be detected which forms the backbone of predictive maintenance. Human intervention in this process is very less. To the given data set, the prediction is done and certain measures are to be taken accordingly.
How Does Predictive Maintenance work?
Predictive maintenance relies on condition-monitoring equipment’s which helps to assess the performance of assets in real-time. With the use of predictive formulae and Internet of Things (IoT), predictive maintenance creates an accurate tool for collecting and analysing asset data.
- Condition-monitoring equipment:
Under predictive maintenance, each asset is monitored using conditioned monitoring equipment. Specifically, the machines are fitted with sensors that capture data about the equipment to enable evaluation of the asset’s efficiency. Because of this step, the traditional way of physical monitoring of assets can be reduced. These sensors measure different kinds of parameters depending on the type of machine. They measure vibration, noise, temperature, pressure etc.
The Internet of Things: It is one of the tools to gather data. Different sensors in the device help in the collection and sharing of data. Predictive maintenance depends on these sensors which connect the assets to a central system and stores the information.
Anomaly Detection: It is the identification of events that do not conform to the expected pattern. It will be different from the pattern. For example – detecting anomalies in heart beats, detecting machine part failures.
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